论文标题
可拖动损失功能和颜色图像生成多头限制的玻尔兹曼机器
Tractable loss function and color image generation of multinary restricted Boltzmann machine
论文作者
论文摘要
受限制的玻尔兹曼机器（RBM）是基于统计力学概念的代表性生成模型。尽管具有强大的可解释性优点，但反向传播的不可用而使其比其他生成模型的竞争力较低。在这里，我们得出了二进制和多元RBM的可区分损失函数。然后，我们通过产生彩色面部图像来证明它们的学习性和表现。
The restricted Boltzmann machine (RBM) is a representative generative model based on the concept of statistical mechanics. In spite of the strong merit of interpretability, unavailability of backpropagation makes it less competitive than other generative models. Here we derive differentiable loss functions for both binary and multinary RBMs. Then we demonstrate their learnability and performance by generating colored face images.
